Abstract

This paper presents identifications of human-human interaction in which one person with
limited auditory and visual perception of the environment (a follower) is guided by an agent
with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate
several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational
basis of the guider to modulate the force on the rein in response to the trust level of the fol-
lower. Based on experimental identification systems on human demonstrations show that
the guider and the follower experience learning for an optimal stable state-dependent novel
3 rd and 2 nd order auto-regressive predictive and reactive control policies respectively. By
modeling the follower’s dynamics using a time varying virtual damped inertial system, we
found that the coefficient of virtual damping is most appropriate to explain the trust level of
the follower at any given time. Moreover, we present the stability of the extracted guiding
policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety
of situations where a human requires the assistance of a robot to perceive the environment.